The present invention relates to the art of diagnostic imaging. In particular, it relates to positron emission tomography (PET) and other diagnostic modes in which a subject is examined and an image of the subject is reconstructed from information obtained during the examination.
Previously, PET has been used to study a radionuclide distribution in subjects. Typically, one or more radiopharmaceuticals are injected into a subject. The radiopharmaceuticals are commonly injected into the subject's blood stream for imaging the circulatory system or for imaging specific organs which absorb the injected radiopharmaceuticals. Detector elements are placed proximate to a surface of the subject to monitor and record emitted radiation. In some instances, the detector elements may be rotated or indexed around the subject to monitor the emitted radiation from a plurality of directions, while in other instances a plurality of detector elements can be placed in fixed locations adjacent to the subject or a portion of the subject. For instance, detector elements may be formed into rings and the rings placed substantially adjacent to one another to form a cylindrical scanner whereby a subject is axially inserted at least partially into the cylinder of detector elements. These are generally known as ring-type scanners. The monitored radiation data from the multiplicity of directions is reconstructed into a three dimensional image representation of the radiopharmaceutical distribution within the subject.
Iterative statistical reconstruction techniques in PET provide a robust framework for accounting for the statistical quality of the measured data, modeling the image acquisition process, and incorporating prior knowledge (if any) about the reconstruction solution. While iterative reconstruction has generally become the standard for PET, complete utilization of such methods has been limited due to large computational demands. This is especially true for fully-3D PET, where both direct and oblique coincidence lines are measured to produce highly sensitive, but very large datasets. A variety of approaches have been proposed for iterative reconstruction of fully-3D PET data. The most direct, and potentially highest quality, implementation requires raw data be operated upon directly by a reconstruction algorithm, thereby making full use of Poisson-based statistical models and avoiding any unnecessary degradation or blurring accompanying data pre-processing steps (e.g. arc-correction). However, these implementations tend to be the most computationally demanding as well. At the other end of the spectrum, pre-processing methods, such as rebinning fully-3D data into a set of 2D sinograms followed by 2D iterative reconstruction, can be used to greatly speed the reconstruction; however, such methods tend to spoil the Poisson statistics of the data and/or introduce undesired blurring or other degradations.
Therefore, what is needed is a means to overcome challenges found in the art, some of which are described above.